Degree structure

Source: https://www.tue.nl/en/education/graduate-school/master-data-science-in-business-and-entrepreneurship/degree-structure Parent: https://www.tue.nl/en/education/graduate-school/master-data-science-in-business-and-entrepreneurship

Program structure

ECTS points

120

Duration

Two years

Language

English

Degree

Master of Science

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This Data Science in Business and Entrepreneurship master focuses on the tools and skills you need in order to analyze data used by today’s businesses, and turn this data into meaningful insights.

You acquire the skills and gain the experience you need to evolve into a well-rounded data scientist. You learn by doing – using real business data and case studies based on data-driven issues. This joint degree consists of 120 credits (EC) divided among the courses and master’s thesis as follows:

11 compulsory courses (66 EC)
4 elective courses (24 EC)
Master’s thesis (30 EC)

The first-year semesters contain five courses of each 6 EC. The second year contains five courses of 6 EC and the master’s thesis of 30 EC.

Compulsory courses

In the first year, you follow 8 core courses (5 in the first semester and 3 in the second):

Semester A

Data Intrapreneurship in Action
Advanced Machine Learning
Data Engineering
Strategy and Business Models
Social Network Analysis for Data Scientists

Semester B

Data Consultancy in Action
Interactive and Explainable AI Design
Deep Learning and Natural Language Processing

In the second year, you follow three core courses and write your master’s thesis:

Semester C

Data Entrepreneurship in Action
Intellectual Property and Privacy
Master’s thesis

Semester D

Data Ethics and Entrepreneurship
Master’s thesis

In Action courses

An unparalleled aspect of the program is a series of ‘In Action’ courses. Working in a team, you learn by doing, applying data science methods to create business or societal value from data for companies and organizations. For example: you advise city municipalities on the impact of cultural events using the parking data of ParkNow, our partner organization. Or you predict the costs and duration of a case for DAS, the legal firm. You can help WWF prevent illegal deforestation in developing countries and even improve credit provision to SMEs carried out by Floryn, the fintech scale-up. The significant involvement of these organizations and others in the curriculum is a valued and valuable feature of the program.

Elective courses

On top of the mandatory courses and master’s thesis, you have to pass four elective courses worth 24 EC in total. You are expected to choose one of the specializations listed below. To qualify for a certain specialization, you should pass at least three of the relevant courses, including the core course (listed first and marked in bold below). You are strongly recommended to write your thesis in line with your specialization.

Data Engineer\ Advanced Data Architectures, Cybersecurity, Process Mining, Time Series and Sensor Data, and Data Forensics. \
Data Scientist\ Prescriptive Algorithms, Process Mining, Time Series and Sensor Data, Causal Interference for Business Development, and Decision Support Systems. \
Data Entrepreneur/Consultant\ Data-Driven Service Innovation, Data Visualization, Decision Support Systems and Entrepreneurial Finance\
Data-Driven Researcher\ Research in Action (6 EC or 12 EC), Prescriptive Algorithms, Decision Support Systems and Causal Interference for Business Development

A detailed description of the courses and required literature can be found in our course catalog: Go to the course descriptions.

Knowledge and skills

Alongside the knowledge gained in data engineering, data analytics, decision-making, business development, and legal and ethical disciplines, you are explicitly trained in a set of essential professional skills, such as:

Creative thinking, communicating, presenting, negotiating, debating, interviewing and pitching, practicing these in the In Action courses with real-life data.
By engaging with a company’s stakeholders, you learn to shape the problem definition and select and apply the methods suited to the problem – not vice versa.
You learn to create value for business and better understand the practicalities of doing data science in a company.
You start programming, learning and applying data science methods from day 1 of the master’s program. More than a year is spent on gaining practical experience, which has proven to be well appreciated by future employers. This enables you to launch your career in a positive direction – potentially more difficult to achieve with only a one-year master’s program.
In addition to the professional competences you develop, your academic capabilities are honed. These skills enable you to retrieve and understand scientific articles and do your own research into the core scientific disciplines covered by the master’s program.

Master's thesis

You write your master’s thesis during the second year (30 EC) in the course of your thesis project at an external organization (just as 95% of your peers are doing). The commitment of our partner organizations is a compelling and inspiring feature of the Data Science in Business and Entrepreneurship program. Your thesis must advance one of the four scientific disciplines referred to above, while generating a business or societal impact at the same time. Examples of theses written by our students in the past include the following:

Predicting the occurrence of a change in a data stream (‘concept drift’) for Mobiquity and adapting Machine Learning models accordingly and dynamically (Data Engineering).
Calculating the expected possession value in football using a componential approach, together with the Dutch national football federation KNVB (Data Analytics).
Supporting breast cancer treatment using deep learning algorithms with our partner hospital, the Catharina Ziekenhuis (Data Analytics).
Earmarking targets for potential acquisitions for clients in the private equity market, together with the Bain Management Consultancy group (Data-Driven Business Development).